1. Field of the Invention
The invention described herein is related to discrimination of a sound from components of an audio signal. More specifically, the invention is directed to analyzing a modeled response to an acoustic signal for purposes of classifying the sound components thereof, reducing the dimensions of the modeled response and then classifying the sound using the reduced data.
2. Description of the Prior Art
Audio segmentation and classification have important applications in audio data retrieval, archive management, modern human-computer interfaces, and in entertainment and security tasks. Manual segmentation of audio sounds is often difficult and impractical and much emphasis has been given recently to the development of robust automated procedures.
In speech recognition systems, for example, discrimination of human speech from other sounds that co-occupy the surrounding environment is essential for isolating the speech component for subsequent classification. Speech discrimination is also useful in coding or telecommunication applications where non-speech sounds are not the audio components of interest. In such systems, bandwidth may be better utilized when the non-speech portion of an audio signal is excluded from the transmitted signal or when the non-speech components are assigned a low resolution code.
Speech is composed of sequences of consonants and vowels, non-harmonic and harmonic sounds, and natural silences between words and phonemes. Discriminating speech from non-speech is often complicated by the similarity of many sounds, such as animal vocalizations, to speech. As with other pattern recognition tasks, the first step in any audio classification is to extract and represent the sound by its relevant features. Thus, the need has been felt for a sound discrimination system that generalizes well to particular sounds, and that forms a representation of the sound that both captures the discriminative properties of the sound and resists distortion under varying conditions of noise.